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Fusion of RADARSAT fine-beam SAR and QuickBird data for land-cover mapping and change detection
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
2007 (English)In: Geoinformatics 2007Proceedings of SPIE - The International Society for Optical Engineering: Remotely Sensed Data And Information, Pts 1 And 2 / [ed] Ju, W; Zhao, S, 2007, Vol. 6752, H7522-H7522 p.Conference paper, Published paper (Refereed)
Abstract [en]

The objective of this research is to evaluate multitemporal RADARSAT Fine-Beam C-HH SAR data, QuickBird MS data, and fusion of SAR and MS for urban land-cover mapping and change detection One scene of QuickBird imagery was acquired on July 18, 2002 and five-date RADARSAT fine-beam SAR images were acquired during May to August in 2002. Landsat TM imagery from 1988 was used for change detection. QucikBird images were classified using an object-based and rule-based approach. RADARSAR SAR texture images were classified using a hybrid approach. The results demonstrated that, for identifying 19 land-cover classes, object-based and rule-based classification of Quickbird data yielded an overall classification accuracy of 86.7% (kappa 0.857). For identifying I I land-cover classes, ANN classification of the combined Mean, Standard Deviation and Correlation texture images yielded an overall accuracy: 71.4%, (Kappa: 0.69). The hybrid classification of RADARSAT fine-beam SAR data improved the ANN classification accuracy to 83.56% (kappa: 0.803). Decision level fusion of RADARSAT SAR and QuickBird data improved the classification accuracy of several land cover classes. The post-classification change detection was able to identify the areas of significant change, for example, major new roads, new low-density and high-density, builtup areas and golf courses, even though the change detection results contained large amount of noise due to classification errors of individual images. QuickBrid classification result was able add detailed change information to the major changes identified.

Place, publisher, year, edition, pages
2007. Vol. 6752, H7522-H7522 p.
Series
Proceedings of SPIE - The International Society for Optical Engineering, ISSN 0277-786X ; 6752
Keyword [en]
QuickBird, RADARSAT SAR, land-cover mapping, change detection, texture analysis, artificial neural network, object-based classification, data fusion
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-39586DOI: 10.1117/12.760747ISI: 000251046800088Scopus ID: 2-s2.0-36249030474ISBN: 978-0-8194-6912-0 (print)OAI: oai:DiVA.org:kth-39586DiVA: diva2:441984
Conference
Geoinformatics 2007: Remotely Sensed Data and Information; Nanjing; China 25 May 2007 through 27 May 2007
Available from: 2011-09-20 Created: 2011-09-12 Last updated: 2015-07-14Bibliographically approved

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Ban, YifangHu, HongtaoRangel, Irene
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CiteExportLink to record
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